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Further Applications of Johnson's SU-normal Distribution to Various Regression Models

  • Choi, Pilsun (Department of International Trade, Konkuk University) ;
  • Min, In-Sik (Department of Economics, Kyung Hee University)
  • Published : 2008.03.30

Abstract

This study discusses Johnson's $S_U$-normal distribution capturing a wide range of non-normality in various regression models. We provide the likelihood inference using Johnson's $S_U$-normal distribution, and propose a likelihood ratio (LR) test for normality. We also apply the $S_U$-normal distribution to the binary and censored regression models. Monte Carlo simulations are used to show that the LR test using the $S_U$-normal distribution can be served as a model specification test for normal error distribution, and that the $S_U$-normal maximum likelihood (ML) estimators tend to yield more reliable marginal effect estimates in the binary and censored model when the error distributions are non-normal.

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